OpenRaiser/Intern-Atlas huggingface dataset:
Proof of Backhaul: Trustfree Measurement of Broadband Bandwidth
Venue: arXiv cs.CR 2022
Year: 2022

Recent years have seen the emergence of decentralized wireless networks consisting of nodes hosted by many individuals and small enterprises, reawakening the decades-old dream of open networking. These networks have been deployed in an organic, distributed manner and are driven by new economic models resting on tokenized incentives. A critical requirement for the incentives to scale is the ability to prove network performance in a decentralized trustfree manner, i.e., a Byzantine fault tolerant network telemetry system. In this paper, we present a Proof of Backhaul (PoB) protocol which measures the bandwidth of the (broadband) backhaul link of a wireless access point, termed prover, in a decentralized and trustfree manner. In particular, our proposed protocol is the first one to satisfy the following two properties: (1) Trustfree. Bandwidth measurement is secure against Byzantine attacks by collaborations of challenge servers and the prover. (2) Open. The barrier-to-entry for being a challenge server is low; there is no requirement of having a low latency and high throughput path to the measured link. At a high-level, our protocol aggregates the challenge traffic from multiple challenge servers and uses cryptographic primitives to ensure that a subset of challengers or, even challengers and provers, cannot maliciously modify results in their favor. A formal security model allows us to establish guarantees of accurate bandwidth measurement as a function of the fraction of malicious actors. Our evaluation shows that our PoB protocol can verify backhaul bandwidth of up to 1000 Mbps with less than 8% error using measurements lasting only 100 ms. The measurement accuracy is not affected in the presence of corrupted challengers. Importantly, the basic verification protocol lends itself to a minor modification that can measure available bandwidth even in the presence of cross-traffic.
Evolutionary sparse data-driven discovery of complex multibody system dynamics
Venue: arXiv cs.CE 2022
Year: 2022

The value of unknown parameters of multibody systems is crucial for prediction, monitoring, and control, sometimes estimated using a biased physics-based model leading to incorrect outcomes. Discovering motion equations of multibody systems from time-series data is challenging as they consist of complex rational functions, constants as function arguments, and diverse function terms, which are not trivial to guess. This study aims at developing an evolutionary symbolic sparse regression approach for the system identification of multibody systems. The procedure discovers equations of motion and system parameters appearing as either constant values in function arguments or coefficients of function expressions. A genetic programming algorithm is written to generate symbolic function expressions, in which a hard-thresholding regression method is embedded. In an evolutionary manner, the complex functional forms, constant arguments, and unknown coefficients are identified to eventually discover the governing equation of a given system. A fitness measure is presented to promote parsimony in distilled equations and reduction in fit-to-data error. Hybrid discrete-continuous dynamical systems are also investigated, for which an approach is suggested to determine both mode number and system submodels. The performance and efficiency of the suggested evolutionary symbolic sparse regression methodology are evaluated in a simulation environment. The capability of the developed approach is also demonstrated by studying several multibody systems. The procedure is efficient and gives the possibility to estimate system parameters and distill respective governing equations. This technique reduces the risk that the function dictionary does not cover all functionality required to unravel hidden physical laws and the need for prior knowledge of the mechanism of interest.
Strategies and Vulnerabilities of Participants in Venezuelan Influence Operations
Venue: arXiv cs.SI 2022
Year: 2022

Studies of online influence operations, coordinated efforts to disseminate and amplify disinformation, focus on forensic analysis of social networks or of publicly available datasets of trolls and bot accounts. However, little is known about the experiences and challenges of human participants in influence operations. We conducted semi-structured interviews with 19 influence operations participants that contribute to the online image of Venezuela, to understand their incentives, capabilities, and strategies to promote content while evading detection. To validate a subset of their answers, we performed a quantitative investigation using data collected over almost four months, from Twitter accounts they control. We found diverse participants that include pro-government and opposition supporters, operatives and grassroots campaigners, and sockpuppet account owners and real users. While pro-government and opposition participants have similar goals and promotion strategies, they differ in their motivation, organization, adversaries and detection avoidance strategies. We report the Patria framework, a government platform for operatives to log activities and receive benefits. We systematize participant strategies to promote political content, and to evade and recover from Twitter penalties. We identify vulnerability points associated with these strategies, and suggest more nuanced defenses against influence operations.
WristSketcher: Creating Dynamic Sketches in AR with a Sensing Wristband
Venue: arXiv cs.HC 2022
Year: 2022

Restricted by the limited interaction area of native AR glasses (e.g., touch bars), it is challenging to create sketches in AR glasses. Recent works have attempted to use mobile devices (e.g., tablets) or mid-air bare-hand gestures to expand the interactive spaces and can work as the 2D/3D sketching input interfaces for AR glasses. Between them, mobile devices allow for accurate sketching but are often heavy to carry, while sketching with bare hands is zero-burden but can be inaccurate due to arm instability. In addition, mid-air bare-hand sketching can easily lead to social misunderstandings and its prolonged use can cause arm fatigue. As a new attempt, in this work, we present WristSketcher, a new AR system based on a flexible sensing wristband for creating 2D dynamic sketches, featuring an almost zero-burden authoring model for accurate and comfortable sketch creation in real-world scenarios. Specifically, we have streamlined the interaction space from the mid-air to the surface of a lightweight sensing wristband, and implemented AR sketching and associated interaction commands by developing a gesture recognition method based on the sensing pressure points on the wristband. The set of interactive gestures used by our WristSketcher is determined by a heuristic study on user preferences. Moreover, we endow our WristSketcher with the ability of animation creation, allowing it to create dynamic and expressive sketches. Experimental results demonstrate that our WristSketcher i) faithfully recognizes users' gesture interactions with a high accuracy of 96.0%; ii) achieves higher sketching accuracy than Freehand sketching; iii) achieves high user satisfaction in ease of use, usability and functionality; and iv) shows innovation potentials in art creation, memory aids, and entertainment applications.
Twin Contrastive Learning for Online Clustering
Venue: arXiv cs.LG 2022
Year: 2022

This paper proposes to perform online clustering by conducting twin contrastive learning (TCL) at the instance and cluster level. Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively. Based on the observation, for a given dataset, the proposed TCL first constructs positive and negative pairs through data augmentations. Thereafter, in the row and column space of the feature matrix, instance- and cluster-level contrastive learning are respectively conducted by pulling together positive pairs while pushing apart the negatives. To alleviate the influence of intrinsic false-negative pairs and rectify cluster assignments, we adopt a confidence-based criterion to select pseudo-labels for boosting both the instance- and cluster-level contrastive learning. As a result, the clustering performance is further improved. Besides the elegant idea of twin contrastive learning, another advantage of TCL is that it could independently predict the cluster assignment for each instance, thus effortlessly fitting online scenarios. Extensive experiments on six widely-used image and text benchmarks demonstrate the effectiveness of TCL. The code will be released on GitHub.
FogROS G: Enabling Secure, Connected and Mobile Fog Robotics with Global Addressability
Venue: arXiv cs.RO 2022
Year: 2022

Fog Robotics renders networked robots with greater mobility, on-demand compute capabilities and better energy efficiency by offloading heavy robotics workloads to nearby Edge and distant Cloud data centers. However, as the de-facto standard for implementing fog robotics applications, Robot Operating System (ROS) and its successor ROS2 fail to provide fog robots with a mobile-friendly and secure communication infrastructure. In this work, we present FogROS G, a secure routing framework that connects robotics software components from different physical locations, networks, Data Distribution Service (DDS) and ROS distributions. FogROS G indexes networked robots with globally unique 256-bit names that remains constant even if the robot roams between multiple administrative network domains. FogROS G leverages Global Data Plane, a global and secure peer-to-peer routing infrastructure between the names, guaranteeing that only authenticated party can send to or receive from the robot. FogROS G adopts a proxy-based design that connect nodes from ROS1 and ROS2 with mainstream DDS vendors; this can be done without any changes to the application code.
Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale
Venue: arXiv cs.LG 2022
Year: 2022

We present Amos, a stochastic gradient-based optimizer designed for training deep neural networks. It can be viewed as an Adam optimizer with theoretically supported, adaptive learning-rate decay and weight decay. A key insight behind Amos is that it leverages model-specific information to determine the initial learning-rate and decaying schedules. When used for pre-training BERT variants and T5, Amos consistently converges faster than the state-of-the-art settings of AdamW, achieving better validation loss within <=70% training steps and time, while requiring <=51% memory for slot variables. Our code is open-sourced at: https://github.com/google-research/jestimator
TAP: Transparent and Privacy-Preserving Data Services
Venue: arXiv cs.CR 2022
Year: 2022

Users today expect more security from services that handle their data. In addition to traditional data privacy and integrity requirements, they expect transparency, i.e., that the service's processing of the data is verifiable by users and trusted auditors. Our goal is to build a multi-user system that provides data privacy, integrity, and transparency for a large number of operations, while achieving practical performance. To this end, we first identify the limitations of existing approaches that use authenticated data structures. We find that they fall into two categories: 1) those that hide each user's data from other users, but have a limited range of verifiable operations (e.g., CONIKS, Merkle2, and Proofs of Liabilities), and 2) those that support a wide range of verifiable operations, but make all data publicly visible (e.g., IntegriDB and FalconDB). We then present TAP to address the above limitations. The key component of TAP is a novel tree data structure that supports efficient result verification, and relies on independent audits that use zero-knowledge range proofs to show that the tree is constructed correctly without revealing user data. TAP supports a broad range of verifiable operations, including quantiles and sample standard deviations. We conduct a comprehensive evaluation of TAP, and compare it against two state-of-the-art baselines, namely IntegriDB and Merkle2, showing that the system is practical at scale.
MCSCSet: A Specialist-annotated Dataset for Medical-domain Chinese Spelling Correction
Venue: arXiv cs.CL 2022
Year: 2022

Chinese Spelling Correction (CSC) is gaining increasing attention due to its promise of automatically detecting and correcting spelling errors in Chinese texts. Despite its extensive use in many applications, like search engines and optical character recognition systems, little has been explored in medical scenarios in which complex and uncommon medical entities are easily misspelled. Correcting the misspellings of medical entities is arguably more difficult than those in the open domain due to its requirements of specificdomain knowledge. In this work, we define the task of Medical-domain Chinese Spelling Correction and propose MCSCSet, a large scale specialist-annotated dataset that contains about 200k samples. In contrast to the existing open-domain CSC datasets, MCSCSet involves: i) extensive real-world medical queries collected from Tencent Yidian, ii) corresponding misspelled sentences manually annotated by medical specialists. To ensure automated dataset curation, MCSCSet further offers a medical confusion set consisting of the commonly misspelled characters of given Chinese medical terms. This enables one to create the medical misspelling dataset automatically. Extensive empirical studies have shown significant performance gaps between the open-domain and medical-domain spelling correction, highlighting the need to develop high-quality datasets that allow for Chinese spelling correction in specific domains. Moreover, our work benchmarks several representative Chinese spelling correction models, establishing baselines for future work.
Adaptive re-calibration of channel-wise features for Adversarial Audio Classification
Venue: arXiv cs.SD 2022
Year: 2022

DeepFake Audio, unlike DeepFake images and videos, has been relatively less explored from detection perspective, and the solutions which exist for the synthetic speech classification either use complex networks or dont generalize to different varieties of synthetic speech obtained using different generative and optimization-based methods. Through this work, we propose a channel-wise recalibration of features using attention feature fusion for synthetic speech detection and compare its performance against different detection methods including End2End models and Resnet-based models on synthetic speech generated using Text to Speech and Vocoder systems like WaveNet, WaveRNN, Tactotron, and WaveGlow. We also experiment with Squeeze Excitation (SE) blocks in our Resnet models and found that the combination was able to get better performance. In addition to the analysis, we also demonstrate that the combination of Linear frequency cepstral coefficients (LFCC) and Mel Frequency cepstral coefficients (MFCC) using the attentional feature fusion technique creates better input features representations which can help even simpler models generalize well on synthetic speech classification tasks. Our models (Resnet based using feature fusion) trained on Fake or Real (FoR) dataset and were able to achieve 95% test accuracy with the FoR data, and an average of 90% accuracy with samples we generated using different generative models after adapting this framework.
Privacy-Preserved Neural Graph Similarity Learning
Venue: arXiv cs.LG 2022
Year: 2022

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel Privacy-Preserving neural Graph Matching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://github.com/RUCAIBox/PPGM.
Analogical Concept Memory for Architectures Implementing the Common Model of Cognition
Venue: arXiv cs.AI 2022
Year: 2022

Architectures that implement the Common Model of Cognition - Soar, ACT-R, and Sigma - have a prominent place in research on cognitive modeling as well as on designing complex intelligent agents. In this paper, we explore how computational models of analogical processing can be brought into these architectures to enable concept acquisition from examples obtained interactively. We propose a new analogical concept memory for Soar that augments its current system of declarative long-term memories. We frame the problem of concept learning as embedded within the larger context of interactive task learning (ITL) and embodied language processing (ELP). We demonstrate that the analogical learning methods implemented in the proposed memory can quickly learn a diverse types of novel concepts that are useful not only in recognition of a concept in the environment but also in action selection. Our approach has been instantiated in an implemented cognitive system AILEEN and evaluated on a simulated robotic domain.
Generative Range Imaging for Learning Scene Priors of 3D LiDAR Data
Venue: arXiv cs.CV 2022
Year: 2022

3D LiDAR sensors are indispensable for the robust vision of autonomous mobile robots. However, deploying LiDAR-based perception algorithms often fails due to a domain gap from the training environment, such as inconsistent angular resolution and missing properties. Existing studies have tackled the issue by learning inter-domain mapping, while the transferability is constrained by the training configuration and the training is susceptible to peculiar lossy noises called ray-drop. To address the issue, this paper proposes a generative model of LiDAR range images applicable to the data-level domain transfer. Motivated by the fact that LiDAR measurement is based on point-by-point range imaging, we train an implicit image representation-based generative adversarial networks along with a differentiable ray-drop effect. We demonstrate the fidelity and diversity of our model in comparison with the point-based and image-based state-of-the-art generative models. We also showcase upsampling and restoration applications. Furthermore, we introduce a Sim2Real application for LiDAR semantic segmentation. We demonstrate that our method is effective as a realistic ray-drop simulator and outperforms state-of-the-art methods.
RVE Analysis in LS-DYNA for High-fidelity Multiscale Material Modeling
Venue: arXiv cs.CE 2022
Year: 2022

In modern engineering designs, advanced materials (e.g., fiber/particle-reinforced polymers, metallic alloys, laminar composites, etc.) are widely used, where microscale heterogeneities such as grains, inclusions, voids, micro-cracks, and interfaces significantly affect the macroscopic constitutive behaviors. Obviously, an accurate description of the multiscale material behaviors is of great importance to the success of material design and structural analysis. The Representative Volume Element (RVE) analysis method provides a rigorous means to obtain homogenized macroscopic material properties at the upper length scale from the properties of the material constituents and structures at a lower length scale. Recently, we have developed an RVE module (keyword: *RVE_ANALYSIS_FEM) in the multiphysics simulation software LS-DYNA to enable high-fidelity virtual testing of numerically re-constructed material samples at user-specified characteristic length scales. In this article, a brief introduction to this new feature will be given.
Revisiting Checkpoint Averaging for Neural Machine Translation
Venue: arXiv cs.CL 2022
Year: 2022

Checkpoint averaging is a simple and effective method to boost the performance of converged neural machine translation models. The calculation is cheap to perform and the fact that the translation improvement almost comes for free, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean of the model parameters from several checkpoints, the selection of which is mostly based on empirical recipes without many justifications. In this work, we revisit the concept of checkpoint averaging and consider several extensions. Specifically, we experiment with ideas such as using different checkpoint selection strategies, calculating weighted average instead of simple mean, making use of gradient information and fine-tuning the interpolation weights on development data. Our results confirm the necessity of applying checkpoint averaging for optimal performance, but also suggest that the landscape between the converged checkpoints is rather flat and not much further improvement compared to simple averaging is to be obtained.
Solving the Probabilistic Profitable Tour Problem on a Tree
Venue: arXiv cs.DS 2022
Year: 2022

The profitable tour problem (PTP) is a well-known NP-hard routing problem searching for a tour visiting a subset of customers while maximizing profit as the difference between total revenue collected and traveling costs. PTP is known to be solvable in polynomial time when special structures of the underlying graph are considered. However, the computational complexity of the corresponding probabilistic generalizations is still an open issue in many cases. In this paper, we analyze the probabilistic PTP where customers are located on a tree and need, with a known probability, for a service provision at a predefined prize. The problem objective is to select a priori a subset of customers with whom to commit the service so to maximize the expected profit. We provide a polynomial time algorithm computing the optimal solution in $O(n^2)$, where $n$ is the number of nodes in the tree.